profilers¶

Classes

`AbstractProfiler`	Specification of a profiler.
`AdvancedProfiler`	This profiler uses Python’s cProfiler to record more detailed information about time spent in each function call recorded during a given action.
`BaseProfiler`	If you wish to write a custom profiler, you should inherit from this class.
`PassThroughProfiler`	This class should be used when you don’t want the (small) overhead of profiling.
`SimpleProfiler`	This profiler simply records the duration of actions (in seconds) and reports the mean duration of each action and the total time spent over the entire training run.

Profiler to check if there are any bottlenecks in your code.

class pytorch_lightning.profiler.profilers.AbstractProfiler[source]¶

Bases: abc.ABC

Specification of a profiler.

abstract setup(**kwargs)[source]¶

Execute arbitrary pre-profiling set-up steps as defined by subclass.

Return type: None

abstract start(action_name)[source]¶

Defines how to start recording an action.

Return type: None

abstract stop(action_name)[source]¶

Defines how to record the duration once an action is complete.

Return type: None

abstract summary()[source]¶

Create profiler summary in text format.

Return type: str

abstract teardown(**kwargs)[source]¶

Execute arbitrary post-profiling tear-down steps as defined by subclass.

Return type: None

class pytorch_lightning.profiler.profilers.AdvancedProfiler(dirpath=None, filename=None, line_count_restriction=1.0, output_filename=None)[source]¶

Bases: pytorch_lightning.profiler.profilers.BaseProfiler

This profiler uses Python’s cProfiler to record more detailed information about time spent in each function call recorded during a given action. The output is quite verbose and you should only use this if you want very detailed reports.

Parameters

dirpath¶ (Union[str, Path, None]) – Directory path for the filename. If dirpath is None but filename is present, the trainer.log_dir (from TensorBoardLogger) will be used.
filename¶ (Optional[str]) – If present, filename where the profiler results will be saved instead of printing to stdout. The .txt extension will be used automatically.
line_count_restriction¶ (float) – this can be used to limit the number of functions reported for each action. either an integer (to select a count of lines), or a decimal fraction between 0.0 and 1.0 inclusive (to select a percentage of lines)

Raises

ValueError – If you attempt to stop recording an action which was never started.

start(action_name)[source]¶

Defines how to start recording an action.

Return type: None

stop(action_name)[source]¶

Defines how to record the duration once an action is complete.

Return type: None

summary()[source]¶

Create profiler summary in text format.

Return type: str

teardown(stage=None)[source]¶

Execute arbitrary post-profiling tear-down steps.

Closes the currently open file and stream.

Return type: None

class pytorch_lightning.profiler.profilers.BaseProfiler(dirpath=None, filename=None, output_filename=None)[source]¶

Bases: pytorch_lightning.profiler.profilers.AbstractProfiler

If you wish to write a custom profiler, you should inherit from this class.

describe()[source]¶

Logs a profile report after the conclusion of run.

Return type: None

profile(action_name)[source]¶

Yields a context manager to encapsulate the scope of a profiled action.

Example:

with self.profile('load training data'):
    # load training data code

The profiler will start once you’ve entered the context and will automatically stop once you exit the code block.

Return type: None

setup(stage=None, local_rank=None, log_dir=None)[source]¶

Execute arbitrary pre-profiling set-up steps.

Return type: None

start(action_name)[source]¶

Defines how to start recording an action.

Return type: None

stop(action_name)[source]¶

Defines how to record the duration once an action is complete.

Return type: None

summary()[source]¶

Create profiler summary in text format.

Return type: str

teardown(stage=None)[source]¶

Execute arbitrary post-profiling tear-down steps.

Closes the currently open file and stream.

Return type: None

class pytorch_lightning.profiler.profilers.PassThroughProfiler(dirpath=None, filename=None, output_filename=None)[source]¶

Bases: pytorch_lightning.profiler.profilers.BaseProfiler

This class should be used when you don’t want the (small) overhead of profiling. The Trainer uses this class by default.

start(action_name)[source]¶

Defines how to start recording an action.

Return type: None

stop(action_name)[source]¶

Defines how to record the duration once an action is complete.

Return type: None

summary()[source]¶

Create profiler summary in text format.

Return type: str

class pytorch_lightning.profiler.profilers.SimpleProfiler(dirpath=None, filename=None, extended=True, output_filename=None)[source]¶

Bases: pytorch_lightning.profiler.profilers.BaseProfiler

This profiler simply records the duration of actions (in seconds) and reports the mean duration of each action and the total time spent over the entire training run.

Parameters

dirpath¶ (Union[str, Path, None]) – Directory path for the filename. If dirpath is None but filename is present, the trainer.log_dir (from TensorBoardLogger) will be used.
filename¶ (Optional[str]) – If present, filename where the profiler results will be saved instead of printing to stdout. The .txt extension will be used automatically.

Raises

ValueError – If you attempt to start an action which has already started, or if you attempt to stop recording an action which was never started.

start(action_name)[source]¶

Defines how to start recording an action.

Return type: None

stop(action_name)[source]¶

Defines how to record the duration once an action is complete.

Return type: None

summary()[source]¶

Create profiler summary in text format.

Return type: str